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Machine learning techniques in magnetic levitation problems

dc.contributor.authorArrayás, Manuel
dc.contributor.authorTrueba, José L.
dc.contributor.authorUriarte, Carlos
dc.date.accessioned2023-09-22T10:45:24Z
dc.date.available2023-09-22T10:45:24Z
dc.date.issued2022
dc.identifier.citationManuel Arrayás, José L. Trueba, Carlos Uriarte, Machine learning techniques in magnetic levitation problems, Chaos, Solitons & Fractals, Volume 167, 2023, 113043, ISSN 0960-0779, https://doi.org/10.1016/j.chaos.2022.113043es
dc.identifier.issn0960-0779
dc.identifier.urihttps://hdl.handle.net/10115/24485
dc.descriptionThis work was funded by Universidad Rey Juan Carlos, Spain , Programa Propio: Analysis, modelling and simulations of singular structures in continuum models, M2604.es
dc.description.abstractWe present a method for calculating the stability region of a perfect diamagnet levitated in a magnetic field created by a circular current loop making use of the machine learning techniques. As an application we compute stability regions, points of stable equilibrium and stable oscillatory motions in two chip-based superconducting trap architectures used to levitate superconducting particles. Our procedure is an alternative to a full numerical scheme based on finite element methods which are expensive to implement for optimizing experimental parameters.es
dc.language.isoenges
dc.publisherElsevieres
dc.rightsAtribución 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectMagnetic levitationes
dc.subjectMachine learninges
dc.subjectStability regionses
dc.titleMachine learning techniques in magnetic levitation problemses
dc.typeinfo:eu-repo/semantics/articlees
dc.identifier.doi10.1016/j.chaos.2022.113043es
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses


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